View-Structured Conformal Prediction for 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2609.10307v1
- Date: Wed, 09 Sep 2026 15:16:52 GMT
- Title: View-Structured Conformal Prediction for 3D Gaussian Splatting
- Abstract summary: 3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap does not certify that a rendered view meets a certain prediction coverage.<n>We propose View-Structured Conformal Prediction (VSCP)<n>It splits the pre-calibration scale into a spatial shape from the bounded and a transferable view-wise factor, which predicts the smallest view-wise multiplier that shape needs.
- Score: 26.600186868779364
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap does not certify that a rendered view meets a certain prediction coverage. We treat novel-view synthesis as structured regression and ask that, with probability at least $1-α$, RGB prediction boxes cover at least a $1-β$ fraction of pixels in a new view. We propose View-Structured Conformal Prediction (VSCP). It splits the pre-calibration scale into a spatial shape from the renderer and a transferable view-difficulty factor, which predicts the smallest view-wise multiplier that shape needs. A held-out quantile over views (View-CP) then gives finite-sample validity even when transferring to new scenes. The same factorization makes the analysis exact: a conformity score is the ratio of oracle to predicted view difficulty, and excess width separates into a test-side and a calibration-side term. Across 13 real scenes, pixel-pooled calibration reaches 89.9\% marginal pixel coverage but only 61.4\% view-event coverage at a 90\% target, while View-CP reaches 91.7--92.0\%. At matched coverage VSCP cuts width by 22.1\% against a constant scale, and matches a ten-model ensemble's 21.0\% reduction using only one model per scene and four rather than ten rasterization passes per query. VSCP also improves on the closest single-model baseline, the 3DGS-U field, by 4.7 points ($p=0.0225$). The view predictor transfers from bounded source families to all nine unbounded Mip-NeRF~360 scenes. There the full scale beats the constant scale with 20.7\% width saving on all nine scenes. It also keeps an 18.3\% saving under a different densification backbone and runs at 216--280 FPS on an RTX~4090.
Related papers
- DepthART: Scaling Foundation Monocular Depth to Tiny Models [51.10174763031444]
DepthART is a compact MDE model for on-device deployment across diverse scenes.<n>We show how DepthART consistently surpasses previous tiny baselines in both zero-shot generalization and metric accuracy.<n>We also provide a scalable model family, with DepthART-S reaching 347/245 FPS (strict FP32) on an GTX A6000 at $2242/4482$, 102 FPS (TF32) on a Orin NX 8GB, and over 15 FPS (FP32) on a Jetson Nano 4GB.
arXiv Detail & Related papers (2026-07-19T06:54:52Z) - Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control [20.917423573702127]
3DGS is a strong representation for real-time novel-view, but its standard training pipeline relies on point estimates and hand-tuned synthesiss.<n>We introduce a rendering-aware 3DGS framework that tracks Bayesian geometry with a Normal-Inverse-Wishart posterior over means and covariances.<n>In a fixed-budget 16-to-32 active-view task, native NIW acquisition improves PSNR by +0.453 dB and LPIPS by -0.0146 over a scoring-only 3-member standard-ensemble baseline.
arXiv Detail & Related papers (2026-07-06T18:01:08Z) - PointSplat: Compact Gaussian Splatting via Human-Centric Prediction [70.45838533838104]
We propose PointSplat, a novel human-centric approach to producing 3D human representations from input views on the fly.<n>The proposed method first estimates a coarse geometric proxy and performs ray casting to prune redundant points.<n>It then employs a Point-Image Transformer to fuse appearance and geometry features, predicting Gaussian attributes in a single forward pass.
arXiv Detail & Related papers (2026-06-30T17:59:05Z) - Variance Reduction on the Camera Axis: Multi-View Score Distillation for 3D [0.30586855806896046]
Score distillation turns a pretrained 2D diffusion model into a 3D generator, but the per-step gradient is estimated from a single randomly chosen view.<n>Prior work addresses this by retraining the diffusion prior on multi-view data.<n>We instead isolate the sampling axis.
arXiv Detail & Related papers (2026-06-29T08:41:42Z) - Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data [0.0]
Path-traced synthetic stereo data underlie a large fraction of disparity-estimation training pipelines.<n>We show that the underlying emphvariance fields are highly correlated once aligned by the ground-truth disparity warp.<n>This signal is unique to MC-rendered data and constitutes a candidate sim-to-real shortcut.
arXiv Detail & Related papers (2026-06-24T07:13:00Z) - Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction [1.6214121783846343]
We evaluate a nightly consolidation of interaction knowledge into model weights via synthesis, synthesis, and Low-Rank Adaptation (LoRA) fine-tuning on a single consumer.<n>Across ten realistic software development conversations, cascading compaction retain 36.8 +/- 3.0% of knowledge.<n>LoRA consolidates interaction knowledge into model weights via synthesis, synthesis, and Low-Rank Adaptation (LoRA) fine-tuning on a single consumer.
arXiv Detail & Related papers (2026-05-23T16:57:25Z) - The frame-level leakage trap: rethinking evaluation protocols for intrinsic image decomposition, with source-separable uncertainty as a case study [0.0]
We quantify the leakage effect for the first time, across three architectures.<n>A frame-level split inflates test R_PSNR by 1.6 to 2.0 dB relative to a scene-level split.<n>We present a physics-informed decomposition I = R composed with S + N with a source-separable three-way heteroscedastic uncertainty head.
arXiv Detail & Related papers (2026-05-07T14:37:16Z) - BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
arXiv Detail & Related papers (2026-02-04T02:54:37Z) - Quantized Visual Geometry Grounded Transformer [67.15451442018258]
This paper proposes the first Quantization framework for VGGTs, namely QuantVGGT.<n>We introduce Dual-Smoothed Fine-Grained Quantization, which integrates pre-global Hadamard rotation and post-local channel smoothing.<n>We also design Noise-Filtered Diverse Sampling, which filters outliers via deep-layer statistics.
arXiv Detail & Related papers (2025-09-25T15:17:11Z) - FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes [50.534213038479926]
FreeSplat is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis.
We propose a simple but effective free-view training strategy that ensures robust view synthesis across broader view range regardless of the number of views.
arXiv Detail & Related papers (2024-05-28T08:40:14Z) - LoLep: Single-View View Synthesis with Locally-Learned Planes and
Self-Attention Occlusion Inference [66.45326873274908]
We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately.
Compared to MINE, our approach has an LPIPS reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%.
arXiv Detail & Related papers (2023-07-23T03:38:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.